Decisions taken during migration can have a large effect on the fitness of birds. Migration must be accurately timed with food availability to allow efficient fueling but is also constrained by the optimal arrival date at the breeding site. The decision of when to leave a site can be driven by energetics (sufficient body stores to fuel flight), time-related cues (internal clock under photoperiodic control), or external cues (temperature, food resources). An individual based model (IBM) that allows a mechanistic description of a range of departure decision rules was applied to the spring migration of pink-footed geese (Anser brachyrhynchus) from wintering grounds in Denmark to breeding grounds on Svalbard via 2 Norwegian staging sites. By comparing predicted with observed departure dates, we tested 7 decision rules. The most accurate predictions were obtained from a decision rule based on a combination of cues including the amount of body stores, date, and plant phenology. Decision rules changed over the course of migration with the external cue decreasing in importance and the time-related cue increasing in importance for sites closer to breeding grounds. These results are in accordance with descriptions of goose migration, following the "green-wave": Geese track the onset of plant growth as it moves northward in spring, with an uncoupling toward the end of the migration if time is running out. We demonstrate the potential of IBMs to study the possible mechanisms underlying stopover ecology in migratory birds and to serve as tools to predict consequences of environmental change.

Decisions taken during migration can have a large effect on the fitness of birds. Migration must be accurately timed with food availability to allow efficient fueling but is also constrained by the optimal arrival date at the breeding site. The decision of when to leave a site can be driven by energetics (sufficient body stores to fuel flight), time-related cues (internal clock under photoperiodic control), or external cues (temperature, food resources). An individual based model (IBM) that allows a mechanistic description of a range of departure decision rules was applied to the spring migration of pink-footed geese (Anser brachyrhynchus) from wintering grounds in Denmark to breeding grounds on Svalbard via 2 Norwegian staging sites. By comparing predicted with observed departure dates, we tested 7 decision rules. The most accurate predictions were obtained from a decision rule based on a combination of cues including the amount of body stores, date, and plant phenology. Decision rules changed over the course of migration with the external cue decreasing in importance and the time-related cue increasing in importance for sites closer to breeding grounds. These results are in accordance with descriptions of goose migration, following the "green-wave": Geese track the onset of plant growth as it moves northward in spring, with an uncoupling toward the end of the migration if time is running out. We demonstrate the potential of IBMs to study the possible mechanisms underlying stopover ecology in migratory birds and to serve as tools to predict consequences of environmental change.